Top 20 Big Data Tools Used By Professionals

There are plenty of big data tools available online for free. However, some of the handpicked big data tools used by the professionals are given in this article.
authorImageVarun Saharawat30 Oct, 2025
Top 20 Big Data Tools Used By Professionals

Big data tools are software tools that help businesses with all types of big datasets and transform them into important business insights. These engineers process big data and process them using big data tools to extract important insights that help organisations make data-driven informative decisions. Let us know some of the best big data tools used by professionals.

What are Big Data Tools?

Data consists of greater varieties, and volumes, and comes with more velocity from a large number of sources. Big data is all three Vs put together. Big data tools are software applications used to extract useful insights from large and complex datasets.  Using traditional database techniques cannot process huge and complex data. Big data tools help to automate and make data extractions and processing efficient and reliable.

KEY TAKEAWAYS:

  • Purpose of Big Data Tools: Big data tools are software applications designed to process and transform large and complex datasets into valuable business insights, aiding organizations in making informed, data-driven decisions.
  • Types of Big Data Sources: Big data can originate from diverse sources including texts, documents, social media, web applications, customer databases, smart devices, sensors, and more, reflecting the broad spectrum of data variety and velocity.
  • Top Big Data Tools: Professionals use a range of powerful big data tools such as Apache Spark, Hadoop, Apache Flink, Talend, Hive, Apache Storm, and more to efficiently manage, process, analyze, and derive actionable insights from massive datasets.

Various Sources of Big Data 

Big data can be extracted from various sources consisting of large variants of data. Check some of the major sources below.
  • Texts
  • Documents
  • News Report
  • Social Media Platforms
  • Web page 
  • Web applications
  • Historical records
  • Customer database
  • Smart devices
  • Medical Records
  • Transaction Processing System
  • Industrial equipments
  • Geographic information
  • Weather information
  • Traffic information
  • Financial market data
  • Scientific Research
  • Sensors

What are the uses of Big Data Tools?

Big data tools are used by businesses to drive actionable insights. Some of the major tasks of big data tools are mentioned below. 
  • With the help of big data tools experts can gather high volumes of unstructured data and transform them into structured format. It makes data processing quick and effective. 
  • It can store massive volumes of data on premises or on cloud and also regularly update them when required. 
  • These large complex data can be analysed using big data tools to meet objectives. 
  • With the help of big data tools important insights can be derived from a pool of data to help businesses make important informative decisions.

Top 20 Big Data Tools in 2024

Big data tools are used to extract important insights from huge complex data. These software tools use many algorithms and applications to automate our tasks and make it more effective than the traditional methods. 

1. Apache Spark

It is an open-source big data analytics tool that supports various programming languages such as Java, Python, Scala, R, etc. It makes developer work easy. It supports real-time batch processing of complex data and data streaming. It can be integrated with other big data tools easily.  However, it requires a large amount of free space and consists of a complex setup. It also consists of limited machine learning and AI support.

2. Hadoop

It is an open-source framework used to store and process big data efficiently. It also provides its separate file system also known as Hadoop Distributed File System (HDFS).  It can easily manage and store large amounts of data. MapReduce is a framework that is used to access all big data stored inside HDFS. Hadoop is highly scalable but has weak security features and complex configuration.

3. Apache Flink 

It is also an open-source framework that is used to process real-time data and perform data processing. It does not follow the batch flow of streams rather it adopts a continuous flow of events.  It is a little complex to learn for beginners and has limited support. 

4. Talend 

Talend is a big data tool that helps to manage, store, transform, and integrate data through various data and platforms. It supports various data sources, applications, and environments. Also, it helps in data cleaning, and validating big data and various resources as per need by the users. It also consists of different colours of Holi.

5. Hive

Hive is a big data warehousing tool. It manages a large dataset in HDFS or other file systems using queries. It is also called HiveQL. It helps read, write, and manage petabytes of data residing in various storage using SQL. It is built on top of Apache Hadoop through HDFS.  It consists of very limited support for machine learning and advanced analytics. It has a very complex setup and administration. 

6. Apache Storm 

It is a free and open-source real-time computation system. It can easily process unbound streams of data. It can be used with any programming language, Java, Python, R, Scala, etc. It is used for many cases, including online machine learning, real-time analytics, continuous computation, ETL, and more.  It consists of a complex setup and configuration. It has limited support for batch processing. It has limited support for huge datasets. 

7. Apache ZooKeeper

ZooKeeper can enable highly reliable distributed coordination and maintain an open-source server. It is an open source project under Apache for maintaining information, naming, and providing distributed synchronisation. It also provides an easy way to manage tasks across many servers which makes it scalable and fault tolerant. 

8. Cassandra 

Cassandra is an open-source No-SQL data handling software application for big data. It can handle large amounts of data across various servers, with very little chance of failure almost negligible. It supports real-time data processing and hence is trusted by many organizations for high scalability and performance. It supports cloud infrastructure too which makes it a perfect platform.

9. Apache Mahout

Apache Mahout is a framework designed to help mathematicians, data scientists, and statisticians in implementing their algorithms. It can support multiple distributed backends and can mathematically serve expressive Scala DSL. It can easily provide an implementation of scalable machine learning algorithms.  It consists of a large collection of algorithms for major machine learning tasks such as classification, filtering, recommendation systems, etc. It is also built on top of Apache Hadoop which allows it to handle a large amount of data. 

10. SAP HANA 

It is an application development platform that can process large volumes of data in real-time. It can support real-time applications and also support cloud infrastructure and also support various advanced analytics processes such as text analytics, predictive analytics, spatial, etc. SAP HANA can also provide real-time processing to provide real-time insights. 

11. Teradata Vantage 

It is an advanced analytics software application that uses data warehousing, and data processing in real time all on a single platform. It supports various data types and programming languages to drive important informative decisions for businesses. It can also maintain large datasets and heavy workloads efficiently which makes it a scalable platform. 

12. Apache Kafka 

It is a powerful streaming platform that helps provide real-time data processing, data pipelines, data integration, and an open source event streaming platform used by many big companies. 

13. Apache Pig 

It is a high-level software application to handle large data sets and is used for the manipulation and analysis of big data. It also provides a scripting language Pig Latin used by developers to execute important complex data processing tasks easily. 

14. Apache Hbase

It is an open source application that can handle big and complex raw and unstructured big data. It is a NoSQL database application providing real-time processing for big data. It provides consistency with a high level of accuracy, especially for platforms such as financial services, and online gaming where real-time data processing is required.

15. Oracle Big Data Appliance

It is an open source high-performance and secure platform that can run various workloads on Hadoop and NoSQL platforms. It uses strong encryption using Kerberos, Apache Sentry, and Network encryption to make the data secure. 

16. Cloudera 

It is a paid big data tool used for managing big data and providing advanced data analytics. It can efficiently store, process, and analyse large and complex data and support effective and scalable solutions for machine learning, data warehousing, data engineering, and more.

17. MapR

It is a software application tool to analyze large-scale data and manage various processes using data analytics and real-time processing. It also integrates AI and machine learning algorithms effectively. 

18. Databricks

Databricks can effectively integrate data analytics, processing, and machine learning all in one platform and handle large and complex datasets effectively. It can easily integrate with third-party sources and consists of built-in libraries for machine learning. 

19. Microsoft HDInsights 

It is a cloud-based big data platform that can easily integrate with Microsoft products and Azure services. It provides easy deployment and management with a user-friendly interface. 

20. IBM BigInsights

It provides data processing, data storage, and analytics platform and can handle high volumes of data efficiently. IBM Biginsights provides advanced analytics and machine learning. It is a SQL-based tool that provides queries for data exploration.

Why do we use Big Data Tools?

Big data is complex raw and unstructured data available from various sources such as text, video, audio, sensors, social platforms, etc. To extract important insights from it which can be used by businesses to uncover unseen trends and patterns. Also, predicting and forecasting the future based on these big complex data big data tools is important.  These big data tools automate and make data extraction, processing, and visualization easy. There are many free big data tools available online. However, professionals need some handpicked tools to make their output effective and reliable. 

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Big Data Tools FAQs

What are Big data tools?

Big data tools are software applications used to extract useful insights from large and complex datasets.

Which is the best big data tool?

Some of the best big data tools are Apache Hadoop, Talend, Flink, Cassandra, MapR, Cloudera, Mahout, etc.

Is Tableau a big data tool?

Tableau is a big data platform that prepares, analyzes, and shares big data insights. It can perform visual analysis and govern big data and can be shared across various organizations.

Is PowerBI a big data tool?

No, PowerBI is a data visualization tool. It can generate reports and present big data structured insights using various visualization forms.